Near Duplicate Image Detection: min-Hash and tf-idf Weighting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03150838" target="_blank" >RIV/68407700:21230/08:03150838 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Near Duplicate Image Detection: min-Hash and tf-idf Weighting

Popis výsledku v původním jazyce

This paper proposes two novel image similarity measures for fast indexing via locality sensitive hashing. The similarity measures are applied and evaluated in the context of near duplicate image detection. The proposed method uses a visual vocabulary ofvector quantized local feature descriptors (SIFT) and for retrieval exploits enhanced min-Hash techniques. Standard min-Hash uses an approximate set intersection between document descriptors was used as a similarity measure. We propose an efficient way of exploiting more sophisticated similarity measures that have proven to be essential in image / particular object retrieval. The proposed similarity measures do not require extra computational effort compared to the original measure. We focus primarily on scalability to very large image and video databases, where fast query processing is necessary. The method requires only a small amount of data need be stored for each image. We demonstrate our method on the TrecVid 2006 data set which c

Název v anglickém jazyce

Near Duplicate Image Detection: min-Hash and tf-idf Weighting

Popis výsledku anglicky

This paper proposes two novel image similarity measures for fast indexing via locality sensitive hashing. The similarity measures are applied and evaluated in the context of near duplicate image detection. The proposed method uses a visual vocabulary ofvector quantized local feature descriptors (SIFT) and for retrieval exploits enhanced min-Hash techniques. Standard min-Hash uses an approximate set intersection between document descriptors was used as a similarity measure. We propose an efficient way of exploiting more sophisticated similarity measures that have proven to be essential in image / particular object retrieval. The proposed similarity measures do not require extra computational effort compared to the original measure. We focus primarily on scalability to very large image and video databases, where fast query processing is necessary. The method requires only a small amount of data need be stored for each image. We demonstrate our method on the TrecVid 2006 data set which c

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F06%2F1821" target="_blank" >GA201/06/1821: Algoritmy rozpoznávání obrazu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2008

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

BMVC 2008: Proceedings of the 19th British Machine Vision Conference

ISBN

978-1-901725-36-0

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

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Název nakladatele

British Machine Vision Association

Místo vydání

London

Místo konání akce

Leeds

Datum konání akce

1. 9. 2008

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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